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Academic literature on the topic 'Aorte – Scanographie'
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Journal articles on the topic "Aorte – Scanographie"
Leblanc, G., P. Y. Laffy, C. Michel, and O. Le Guen. "Traitements 2D et 3D des données acquises en scanographie volumique Perspective d'application en pathologie aorto-iliaque." RBM-News 19, no. 5 (October 1997): 148–56. http://dx.doi.org/10.1016/s0222-0776(97)89501-6.
Full textNdayishimiye, V., H. Belgadir, Z. Zerouali Boukhal, A. F. Achta, A. Merzem, O. Amriss, N. Moussali, and N. El Benna. "Laryngeal Radioanatomy." SAS Journal of Medicine 8, no. 7 (July 19, 2022): 463–69. http://dx.doi.org/10.36347/sasjm.2022.v08i07.007.
Full textDissertations / Theses on the topic "Aorte – Scanographie"
Minifie, Catherine. "Suivi évolutif des hématomes de paroi aortique : à propos de 24 cas." Bordeaux 2, 1999. http://www.theses.fr/1999BOR23055.
Full textCraiem, Damian. "Développement et évaluation de nouveaux outils d'analyse géométrique 3D pour la prévention et le traitement des maladies aortiques." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB058/document.
Full textNew imaging technologies, including those associated with multislice computed tomography, allow to evaluate the structure of the thoracic aorta in 3D with an impressive resolution. Aortic virtual reconstruction and geometric modeling are essential for imaging evaluation because manual measurements are time-consuming, and the available tools still need to be adapted to complex aortic morphologies. The aorta is more than a simple tubular conduit vessel for blood. It also regulates the pulsatile pressure waves that are injected into the arterial system by the left ventricle. The biomechanical disorders produced by these waves can accelerate the formation of calcium deposits within the arterial wall. Furthermore, they are thought to be responsible for severe aortic complications, including aneurysms and dissections. Endovascular aortic repair is a modern technique based on the implantation of an endograft to restore the normal blood flow. Precise morphological measurements are required to improve this technique, for both surgery planning and patient follow up. Our objective was to develop original algorithms to study the aortic geometry in 3D. A computing platform was designed and tested to analyze three main aortic pathologies: calcified atherosclerosis, aneurysms and dissections. The hypothesis of our study was that the individual arterial geometry of a subject plays a complementary role in the development of vascular pathologies beyond traditional risk factors. Our first work revealed that 80% of the total geometric variability in the thoracic aorta might be explained using 3 factors: the aortic volume, the aortic arc unfolding and its asymmetry. Variability percentages accounted for 46%, 22% and 12%, respectively. The next 2 works, showed that calcifications in the thoracic aorta were concentrated in the aortic arch and in the proximal descending segment. This spatial distribution was associated with aortic morphology, independently of age, sex, body surface area and traditional risk factors. Our fourth article revealed that calcium deposits in the entire thoracic aorta (including the aortic arch) was associated with non-cardiac events, beyond the standard coronary artery calcium score. It is noteworthy that the aortic arch region is systematically excluded from standard scans. Our fifth manuscript described a novel deformable model applied to the aortic segmentation under pathological contexts. It was used to estimate the size and shape of abdominal aneurysms before and after endograft implantation. In the last work, this method was adapted to study the geometry of the thoracic aorta of patients with an aortic dissection with respect to a control group. Three anatomic variables were identified for the risk prediction model: the aortic arch diameter, the thoracic aortic length and the age of the patient. In conclusion, our results show that aortic diseases are closely associated with aortic geometry, independently from traditional risk factors. The developed algorithms improved the automation of measurements and reduced the variability of the estimations
Ma, Qixiang. "Deep learning based segmentation and detection of aorta structures in CT images involving fully and weakly supervised learning." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS029.
Full textEndovascular aneurysm repair (EVAR) and transcatheter aortic valve implantation (TAVI) are endovascular interventions where preoperative CT image analysis is a prerequisite for planning and navigation guidance. In the case of EVAR procedures, the focus is specifically on the challenging issue of aortic segmentation in non-contrast-enhanced CT (NCCT) imaging, which remains unresolved. For TAVI procedures, attention is directed toward detecting anatomical landmarks to predict the risk of complications and select the bioprosthesis. To address these challenges, we propose automatic methods based on deep learning (DL). Firstly, a fully-supervised model based on 2D-3D features fusion is proposed for vascular segmentation in NCCTs. Subsequently, a weakly-supervised framework based on Gaussian pseudo labels is considered to reduce and facilitate manual annotation during the training phase. Finally, hybrid weakly- and fully-supervised methods are proposed to extend segmentation to more complex vascular structures beyond the abdominal aorta. When it comes to aortic valve in cardiac CT scans, a two-stage fully-supervised DL method is proposed for landmarks detection. The results contribute to enhancing preoperative imaging and the patient's digital model for computer-assisted endovascular interventions